A quantitative, model-driven approach to technology selection and development through epistemic uncertainty reduction

When aggressive aircraft performance goals are set, he integration of new, advanced technologies into next generation aircraft concepts is required to bridge the gap between current capabilities and required capabilities. A large number of technologies exists that can be pursued, and only a subset may practically be selected to reach the chosen objectives. Additionally, the appropriate numerical and physical experimentation must be identified to further develop the selected technologies. These decisions must be made under a large amount of uncertainty because developing technologies introduce phenomena that have not been previously characterized. Traditionally, technology selection decisions are made based on deterministic performance assessments that do not capture the uncertainty of the technology impacts. Model-driven environments and new, advanced uncertainty quantification techniques provide the ability to characterize technology impact uncertainties and pinpoint how they are driving the system performance, which will aid technology selection decisions. Moreover, the probabilistic assessments can be used to plan experimentation that facilitates uncertainty reduction by targeting uncertainty sources with large performance impacts. The thesis formulates and implements a process that allows for risk-informed decision making throughout technology development. It focuses on quantifying technology readiness risk and performance risk by synthesizing quantitative, probabilistic performance information with qualitative readiness assessments. The Quantitative Uncertainty Modeling, Management, and Mitigation (QuantUM3) methodology was tested through the use of an environmentally-motivated aircraft design case study based upon NASAs Environmentally Responsible Aviation (ERA) technology development program. A physics-based aircraft design environment was created that has the ability to provide quantitative system-level performance assessments and was employed to model the technology impacts as probability distributions to facilitate the development of an overall process required to enable risk-informed technology and experimentation decisions. The outcome of the experimental e orts was a detailed outline of the entire methodology and a confirmation that the methodology enables risk-informed technology development decisions with respect to both readiness risk and performance risk. Furthermore, a new process for communicating technology readiness through morphological analysis was created as well as an experiment design process that utilizes the readiness information and quantitative uncertainty analysis to simultaneously increase readiness and decrease technology performance uncertainty.Ph.D